前沿多任务迁移学习：数据驱动任务分配的模型与实践
On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation
摘要：
在边缘设备上，数据稀缺是一个常见的问题，迁移学习是一种广泛建议的补救措施。然而，迁移学习对资源受限的边缘设备施加了沉重的计算负担。现有的任务分配方法通常假设所有提交的任务都同等重要，直接应用于多任务迁移学习（MTL）时，会导致任务级资源分配效率低下。为了解决这些问题，我们首先揭示，这是至关重要的，以衡量整体决策性能的改善任务的影响和量化\emph{task importance}。然后，我们表明，任务分配与任务的重要性MTL（TATIM）是一个变种的NP完全背包问题，复杂的计算来解决这个问题需要在不同的背景下反复进行。为了解决TATIM与高计算效率，我们提出了一个数据驱动的协同任务分配（DCTA）的方法。最后，我们不仅通过跟踪驱动的模拟，而且还通过一个新的全面的现实世界的AIOps案例研究，桥梁模型和实践通过一个新的架构和AIOps系统内的主要组件设计的DCTA的性能进行评估。大量的实验表明，我们的DCTA减少了3.24倍的处理时间，并节省了48.4%的能源消耗相比，最先进的解决TATIM。
英文摘要：
On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes a heavy computation burden to resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify \emph{task importance}. We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a Data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study that bridges model and practice via a new architecture and main components design within the AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4\% energy consumption compared with the state-of-the-art when solving TATIM.
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